Research map / Detection and segmentation / Open-set and few-shot detection
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add TV regularization (ablation) | DeblurGAN · GoPro | PSNR: 28.7 vs. 27.9 | Hurt |
| remove the adversarial loss (ablation) | DeblurGAN · motion deblurring | visual sharpness | Hurt |
| remove perceptual loss (ablation) | DeblurGAN · motion deblurring | convergence to meaningful output | Hurt |
| replace perceptual loss with pixel MSE (ablation) | DeblurGAN · motion deblurring | convergence to meaningful output | Hurt |
| combine synthetic blurred images with real-world training images | DeblurGAN Comb · GoPro test | SSIM: 0.954 -> 0.958 | Helped |
| combine synthetic blurred images with real-world training images | DeblurGAN Comb · Kohler | SSIM: 0.816 -> 0.802 | Hurt |
| combine synthetic blurred images with real-world training images | DeblurGAN Comb · YOLO detection benchmark | recall: 0.631 -> 0.742 | Helped |
Closest papers by meaning
- Non-Blind Deblurring: Handling Kernel Uncertainty With CNNs (CVPR 2018 · similarity 0.89)
- Deblurring by Realistic Blurring (CVPR 2020 · similarity 0.88)
- Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring (CVPR 2024 · similarity 0.87)
- Parameterized Blur Kernel Prior Learning for Local Motion Deblurring (CVPR 2025 · similarity 0.86)
- Real-World Efficient Blind Motion Deblurring via Blur Pixel Discretization (CVPR 2024 · similarity 0.86)
- Exploiting Deblurring Networks for Radiance Fields (CVPR 2025 · similarity 0.85)
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